Developing an Ontology-based Metadata Management System for Heterogeneous Clinical Databases

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Developing an Ontology-based Metadata Management
System for Heterogeneous Clinical Databases

• By Quddus Chong
• Winter 2002

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Outline

• Towards a clinical data warehouse
• Integrating heterogeneous data sources
• Clinical abstractions as Ontologies
• Managing database metadata
• The data mediator approach
• Using Protégé-2000

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Towards a Clinical Data Warehouse

• Clinical Data Warehousing is the application of
  Data Warehousing concepts to allow clinical data
  about a large patient population to be analyzed
  to perform clinical quality management and
  medical research.
• In a data warehouse environment, data has the
  following properties
• Data is organized by subject, or domain-level
  concepts, rather than by function.
• Data from various operational systems is
  integrated, by definition or by content.
• Data is archived in non-volatile storage to allow
  temporal analysis.
• Data is recorded with a temporal dimension (e.g.
  timestamp)
• Data is optimized for decision making (DSS) or
  analysis (OLAP).

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Integrating Heterogeneous Data Sources

• The main challenge in integrating data from
  heterogeneous sources is in resolving schema and
  data conflicts.
• Approaches to this problem include using a
  federated database architecture, or providing a
  multi-database interface. These approaches are
  geared more towards providing query access to the
  data sources than towards supporting analysis.
• Types of data integration
• Physical integration convert records from
  heterogeneous data sources into a common format
  (e.g. .xml).
• Logical integration relate all data to a common
  process model (e.g. a medical service like
  diagnose patient or analyze outcomes).
• Semantic integration allow cross-reference and
  possibly inferencing of data with regards to a
  common metadata standard or ontology (e.g. HL7
  RIM, OILDAML).

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Clinical abstractions as Ontologies

• An ontology is a explicit specification of the
  conceptualization of a domain. Information
  models (such as the HL7 RIM) and standardized
  vocabularies (such as UMLS) can be part of an
  ontology. An ontology provides a core component
  in a Knowledge-Based System.
• In the clinical research field, ontologies have
  been used in computerized guideline modeling.
  This allows the development of applications to
  provide recommendations (e.g. to make indications
  for the use of surgical procedures), to identify
  deviations in practices, and screening services
  (e.g. evaluate patient eligibility).
• Benefits of using ontologies include
• Facilitating sharing between systems and reuse of
  knowledge
• Aiding new knowledge acquisition
• Improving the verification and validation of
  knowledge-based systems.

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Managing database metadata

• Metadata is the detailed description of the
  instance data the format and characteristics of
  the populated instance data instances and values
  dependent on the requirements/role of the
  metadata recipient.
• Metadata is used in locating information,
  interpreting information, and integrating/transfor
  ming data.
• Being able to maintain a well-organized and
  up-to-date collection of the organizations
  metadata is a great step towards improving
  overall data quality and usage. However this
  task is complicated by the different quality and
  formats of metadata available (or not) from the
  heterogeneous data sources, and the consistency
  in updating existing metadata.
• A common metadata architecture is essential to
  keeping data manageable.

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The Data Mediator approach

• In this project, we will attempt to develop an
  extensible and adaptable architecture to perform
  integration of heterogeneous data sources into a
  data warehouse environment using a ontology-based
  data mediator approach.
• The components of this architecture include
• Knowledge base stores the ontology consists
  of
• The abstraction model domain-level concepts
• The database description model metadata record
  of data sources
• The mappings model how data elements relate to
  attributes in the abstraction model
• The transformations model metadata of available
  methods to transform data elements from one data
  source to another
• Data mediators provides each data source an
  interface to the warehouse and resolving data
  conflicts between any different representations
  necessary classes generated from the ontology.
• Data warehouse provides access to integrated
  data for analysis and decision-making.

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Patient model (adapted from SMI Dharma model)

• The patient-data information model defines the
  classes and attributes of patient data for an
  Electronic Patient Record (EPR).
• The patient-data model consists of
• a Patient class whose instances hold demographic
  information about specific patients
• a Note_Entry class that describes qualitative
  observations about patients
• a Numeric_Entry class that represent results of
  quantitative measurements
• an Adverse_Event class that models adverse
  reactions to specific substances
• a Condition class that represent medical
  conditions that persist over time, and two
  intervention classes
• Medication and Procedure, that model drugs and
  other medical procedures that have been
  recommended, authorized, or used.
• The defining characteristic of entities in the
  patient-data model is that they are assertions
  about demographic and clinical conditions of
  specific patients.

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Database metadata model (adapted from Critchlow
et. al.)

• The metadata model here contains the information
  needed for the data integration process.
• The database description model contains language
  independent class definitions that closely mirror
  the physical layout of a source database. In our
  prototype model, the database description is
  simply a class containing a set of database
  entries. A model is provided for two distinct
  entry-types field-entries (from flat-file data
  sources) and column-entries (from relational data
  sources). Entries are essentially instances of
  the attribute class.
• Modeling the database metadata as an ontology
  provides flexibility when trying to describe
  heterogeneous data sources. For instance, the
  model can be easily extended to describe Native
  XML databases.
• How the models are used in data integration
• The source database attributes are mapped to the
  appropriate abstraction characteristic through
  mappings. When an abstraction defines multiple
  representations for the same characteristic
  attribute, transformation functions are defined
  to convert between them.

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A prototype architecture
(Data Warehouse environment, e.g. SQL Server)
ontologies can be created and modified via
Protégé-2000 tool underlying format is RDF
possible use of JDBC metadata to obtain db
descriptions
Ontology Server
Source db 1
Target db
Mediator Interface 1
Abstractions
alternatively, a common metadata exchange
standard such as XMI could be used
(Relational DBMS, e.g. MySQL)
Data Descriptions
abstraction model in the ontology is extensible
to any domain
Data Mappings
Source db 2
Mediator Interface 2
Warehouse Mediator
Transformation Descriptions
(Object-Relational DBMS, e.g. Postgresql)
possible use of XSLT to perform data
transformations
XML data binding could be used to generate APIs
for data validation or transformation
key goal develop the ontology server as a
component, use EJB or .NET
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Using Protégé-2000

• Protégé-2000 is a experimental knowledge-acquisiti
  on tool, written in Java, that allows users to
  import, export and create their own ontologies.
• The tool itself is extensible a programming
  developer kit is available for instructions on
  creating plug-ins
• tabs - user interface between a ontology model
  in Protégé and another knowledge-based
  application.
• slot-widget user interface for viewing and
  acquiring slot values for new instances.
• backend plug-ins specify the mechanism that
  Protégé-2000 will use to store the ontology.

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Screenshot Creating the classes and slots of an
ontology
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Screenshot Viewing the newly created ontology
model
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References

• Pedersen T. B., Jensen C. S., Research Issues in
  Clinical Data Warehousing In Proceedings of the
  10th International Conference on Scientific and
  Statistical Database Management, pg. 43-52, July
  1998 (available online http//citeseer.nj.nec.com
  /pedersen98research.html)
• Critchlow T., Ganesh M., Musick R., Meta-Data
  Based Mediator Generation In Proceedings of the
  3rd IFCIS Conference on Cooperative Information
  Systems, August 1998 (available online
  http//citeseer.nj.nec.com/critchlow98metadata.htm
  l)
• Tu S. et. al. A Flexible Approach to Guideline
  Modeling AMIA Annual Symposium, 1999 (available
  online http//smi-web.stanford.edu/pubs/SMI_Abstr
  acts/SMI-1999-0793.html)